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Task Prioritization in Multiagent Environments: A Novel Approach Using Nash Q-Learning

IEEE Transactions on Consumer Electronics 2025年

作者： Li, Kexin Hu, Yu Wang, Jielei Li, Lin Zhang, Shuyuan Luo, Guangchun Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

—With the rapid proliferation of smartphones and various terminal devices, edge computing has gained increasing importance in overcoming computational limitations and enhancing service quality through task offloading. However, most existing approaches handle tasks on a first-come, first-served basis, neglecting task prioritization, which is critical in real-world scenarios where tasks have different levels of urgency and resource requirements. This paper addresses the challenge of effective task prioritization in multi-agent task offloading by proposing a Nash Q-learning-based strategy. Our methodology involves optimizing task allocation decisions using Nash Q-learning to integrate Nash equilibrium principles, thereby enhancing adaptability and dynamic decision-making capabilities within a multi-agent environment. The proposed algorithm is evaluated through extensive simulations, comparing total delay, resource utilization, and task success rates across various task offloading strategies. The results demonstrate that our Nash Q-learning approach outperforms conventional techniques by dramatically lowering overall latency, optimizing resource allocation, and maintaining high task success rates. This work demonstrates the efficacy of Nash Q-learning for dynamically setting priorities for tasks in multi-agent edge computing environments, providing a robust framework for effective resource management and collaborative task completion. And also highlight the potential of Nash Q-learning in advancing intelligent task management in edge computing, suggesting its applicability in real-world, resource-constrained environments. © 2025 IEEE. All rights reserved,

关键词： Computation offloading

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Energy-Aware Task Offloading for Rotatable STAR-RIS-Enhanced Mobile Edge computing Systems

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IEEE Internet of Things Journal 2025年

作者： Yang, Dongdong Li, Bin Niyato, Dusit Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Huzhou Key Laboratory of Urban Multidimensional Perception and Intelligent Computing Huzhou313000 China Nanyang Technological University College of Computing and Data Science Singapore Singapore

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can expand the coverage of mobile edge computing (MEC) services by reflecting and transmitting signals simultaneously, enabling full-space coverage. The orientation of the STAR-RIS plays a crucial role in optimizing the gain of received and transmitted signals, and a rotatable STAR-RIS offers potential enhancement for MEC systems. This paper investigates a rotatable STAR-RIS-assisted MEC system, operated under three protocols, namely energy splitting, mode switching, and time switching. The goal is to minimize energy consumption for multiple moving user devices through the joint optimization of STAR-RIS configurations, orientation, computation resource allocation, transmission power, and task offloading strategies. Considering the mobility of user devices, we model the original optimization problem as a sequential decision-making process across multiple time slots. The high-dimensional, highly coupled, and nonlinear nature makes it a challenging non-convex decision-making problem for traditional optimization algorithms. Therefore, a deep reinforcement learning (DRL) approach is employed, specifically utilizing soft actor-critic algorithm to train the DRL model. Simulation results demonstrate that the proposed algorithm outperforms the benchmarks in both convergence speed and energy efficiency, while reducing energy consumption by up to 52.7% compared to the fixed STAR-RIS scheme. Among three operating protocols, the energy splitting yields the best performance. © 2014 IEEE.

关键词： Mobile edge computing

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Multi-modal Salient Object Detection via a Unified Diffusion Model

Multi-modal Salient Object Detection via a Unified Diffusion...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Shuo Huang, Jiaming Tang, Wenbing Tian, Lili Wei, Yuang Liu, Jing Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Huolala Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science and Technology East China Normal University China

ISBN: (纸本)9798350368741

Salient Object Detection (SOD) aims to identify and segment the most striking elements within an image. Salient object detection methods can be differentiated into several types according to the input data, such as RGB-D (Depth) and RGB-T (Thermal). Previous research primarily focused on saliency detection for single data types. However, forcing an RGB-D SOD model to process RGB-T data will degrade its performance significantly. In addition, current methods still face challenges in detecting fine edge details of salient objects and achieving end-to-end training. To address these issues, we introduce diffSOD, which leverages stable diffusion and cross-modal feature rectification and fusion module for saliency detection by transforming salient object detection into a denoising process from a noisy mask to an object mask. It offers a unified solution for salient object detection that seamlessly spans both RGB-D SOD and RGB-T SOD. Extensive experiments validate the effectiveness of the proposed diffSOD, demonstrating its ability to efficiently detect salient objects across both RGB-D and RGB-T data, while achieving superior performance over state-of-the-art methods. © 2025 IEEE.

关键词： attention interaction fusion latent diffusion model multi-modal salient object detection

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Emotional Knowledge Self-Distillation in Dialogue

Emotional Knowledge Self-Distillation in Dialogue

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jian, Zhongquan Wang, Yancheng Wu, Weichao Yao, Junfeng Wang, Meihong Wu, Qingqiang School of Informatics Xiamen University Xiamen China School of Film Xiamen University Xiamen China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia School of Mechanics and Civil Engineering China University of Mining & Technology Beijing China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen China

ISBN: (纸本)9798350368741

Recognizing emotions in dialogues is vital for effective human-computer interaction, yet remains a challenging task in Natural Language Processing (NLP). Previous studies in Emotion Recognition in Conversation (ERC) have primarily focused on contextual features, while overlooking the importance of emotional features in emotion recognition. To address this gap, we focus on the role of emotional features in ERC and propose a novel method, Emotional Knowledge Self-Distillation (EmoKSD1), to enhance the model's emotional sensitivity. In EmoKSD, utterances are enriched with implicit 〈mask〉 tokens to represent conveyed emotions, allowing the distillation of emotional knowledge from explicit emotional tokens to implicit 〈mask〉 tokens, thereby enhancing the model's ability to perceive subtle emotions within the dialogue. Through thorough evaluations on two public ERC datasets (i.e., IEMOCAP and MELD) using proposed coarse-grained utterance distillation and fine-grained token distillation techniques, EmoKSD demonstrates superior performance compared to existing methods, highlighting the significance of emotional features in ERC. © 2025 IEEE.

关键词： Dialogue System Emotion Recognition in Conversation Knowledge Distillation Natural Language Processing

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Improved generative adversarial network with bald eagle search optimization for missing data imputation

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Earth science Informatics 2025年第2期18卷 1-18页

作者： Qin, Xiwen Shi, Hongyu Dong, Xiaogang Zhang, Siqi Yuan, Liping Guo, Sijia School of Mathematics and Statistics Changchun University of Technology Jilin Changchun China Jilin Provincial Key Laboratory of Data Science and Intelligent Decision Jilin Changchun China School of Mathematics Sun Yat-Sen University Guangdong Guangzhou China

Missing data imputation is a critical task, as incomplete datasets can negatively impact model performance. Traditional imputation methods often fail to capture the intricate relationships within data, resulting in poor representations of complex patterns and reduced predictive accuracy. Generative Adversarial Network (GAN)-based imputation methods frequently encounter challenges, such as hyperparameter tuning difficulties and mode collapse. In this paper, we propose a improved GAN-based imputation framework, termed BESGAIN. To be concrete, BESGAIN introduces the Bald Eagle Search (BES) optimization algorithm to dynamically adjust hyperparameters, ensuring high-quality data imputation. Additionally, we incorporate an enhanced loss function that leverages cosine similarity to improve data diversity. This helps prevent performance degradation caused by overfitting to a single data mode. As a result, BESGAIN achieves a richer and more accurate set of imputed values. We validate the performance of BESGAIN across seven real-world datasets and compare it against state-of-the-art and widely used imputation methods. The experimental results demonstrate that BESGAIN outperforms existing techniques in terms of accuracy, robustness, and generalizability, particularly in datasets with high missing rates. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Bald eagle search optimization algorithm Generative adversarial imputation networks Missing data Mode collapse

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Multiscale Temporal-Convolution and Attention Improved Neural Network for Biased Mental State Recognition 17th

Multiscale Temporal-Convolution and Attention Improved Neur...

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17th International Conference on Brain Informatics, BI 2024

作者： Zuo, Guoyu Xiao, Erjun Zhang, Tielin Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing University of Technology Beijing100124 China Center for Excellence in Brain Science and Intelligence Technology University of Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9789819632930

Accurate detection of mental state is vital for sleep assessment and anesthesia monitoring. However, challenges exist, including 1) limited data sources caused by few modalities, few channels, biased sample distribution, or large individual differences;2) multiscale dynamic feature of mental state signals, containing complex and dynamic fluctuations. In this paper, we proposed a multiscale temporal convolution and attention improved neural network (MTA-NN) to handle these two challenges, including: a preprocessing method using a clustering-first and label-generation approach for data augmentation involves filtering the production samples to mitigate the impact of noise on model performance;multiscale temporal-convolution module for detecting different scales of temporal and frequency features;an attention module for decoding key features in different spatial and temporal channels, and for setting the importance of historical dependencies;a full-connection layer is designed to recognize different sleep stage. The proposed MTA-NN is then verified in three typical sleep-state recognition tasks, including SleepEDF-20, SleepEDF-78, and ISRUC3. Compared to some state-of-the-art algorithms, the proposed MTA-NN achieves higher performance. Further ablation experiments show the necessary and importance of these designed sub modules in MTA-NN. We consider the same algorithm can also be extended to further similar mental state recognition tasks such as anesthesia monitoring. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anesthesiology

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Thermal engineering in ALD-grown ZGO thin films for high-performance photodetectors

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材料科学技术（英文版） 2025年第6期209卷 19-26页

作者： Si-Tong Ding Yu-Chang Chen Cai-Yu Shi Lei Shen Qiu-Jun Yu Lang-Xi Ou Ze-Yu Gu Na Chen Ting-Yun Wang David Wei Zhang Hong-Liang Lu State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics & SystemsSchool of MicroelectronicsFudan UniversityShanghai 200433China Zhangjiang Fudan International Innovation Center Shanghai 201203China National Integrated Circuit Innovation Center Shanghai 201203China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute Communication and Data ScienceShanghai UniversityShanghai 200444China State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics & SystemsSchool of MicroelectronicsFudan UniversityShanghai 200433China Zhangjiang Fudan International Innovation Center Shanghai 201203China National Integrated Circuit Innovation Center Shanghai 201203China Jiashan Fudan Institute Jiaxing 314100China

Doped gallium oxide-based thin films are a class of wide-band semiconductor materials with the ad-vantages of chemically stable,tunable bandgap,and offer the benefit of ultraviolet *** order to obtain photodetectors(PDs)with superior response,higher demands are placed on the quality of growth and processing of doped *** this work,Zn-doped ternary metal oxide ZnGaO(ZGO)thin films were grown using the atomic layer deposition technique and annealed at different temperatures under an oxy-gen *** results showed that the high-quality ZGO films with good uniformity,high visible light transmittance,low roughness,and significant reduction of oxygen vacancies were obtained after ***,metal-semiconductor-metal PDs were prepared based on the studied ZGO *** responsivity(R),detectivity(D*),and external quantum efficiency(EQE)of the optimized device are 61.8 A W-1,1.2 x 1012 Jones,and 255.9％,*** to the unannealed device,the an-nealed ZGO PD achieves a maximum 309-fold increase in *** thermal engineering work may provide a strong reference for the development of low-cost,large-area,high-performance ultraviolet *** it also broadens the application of ternary metal oxides in optoelectronics.

关键词： Atomic layer deposition Oxygen annealing ZGO thin films Photodetector Ultraviolet detection

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Collaborative Pushing and Grasping in Complex Scenarios via a Generator-Evaluator Network with Multiple Action Primitives 17th

Collaborative Pushing and Grasping in Complex Scenarios vi...

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17th International Conference on intelligent Robotics and Applications, ICIRA 2024

作者： Zuo, Guoyu Wang, Zihao Luo, Yongkang Yu, Shuangyue Zhao, Min School of Information Science and Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819607761

As robotic technologies advance, robotic arm manipulation tasks in complex environments become increasingly important. This paper presents a new collaborative pushing and grasping strategy to address two major challenges robotic arms encounter in complex environments: invisibility and tight enclosure of target objects. We propose a generator-evaluator network architecture based on multiple action primitives, incorporating hierarchical reinforcement learning and Double DQN techniques to optimize the operational efficiency of the robotic arm. By introducing a new action primitive to prioritize pushing operations around the topmost objects in space, our approach effectively reveals and locates obscured target objects. Additionally, utilizing the Spatial Correlation Test (SCT), our method explores all possible collision-free operational positions in the action space, selecting the optimal position from among them. Experimental results demonstrate that our approach achieves high efficiency and success rates in scenarios with randomly distributed objects, objects tightly enclosed by others, and invisible objects. This strategy enhances the speed and accuracy of tasks and improves the robot’s adaptability to complex environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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A Modified PRP-HS Hybrid Conjugate Gradient Algorithm for Solving Unconstrained Optimization Problems

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应用数学 2025年第2期38卷 553-564页

作者： LI Xiangli WANG Zhiling LI Binglan School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin 541004China Guangxi Colleges and University Key Laboratory of Data Analysis Guilin 541004China Center for Applied Mathmatics of Guangxi Guilin 541004China

In this paper,we propose a three-term conjugate gradient method for solving unconstrained optimization problems based on the Hestenes-Stiefel(HS)conjugate gradient method and Polak-Ribiere-Polyak(PRP)conjugate gradient *** the condition of standard Wolfe line search,the proposed search direction is the descent *** general nonlinear functions,the method is globally ***,numerical results show that the proposed method is efficient.

关键词： Conjugate gradient method Unconstrained optimization Sufficient descent condition Global convergence

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Oracle-guided Dynamic User Preference Modeling for Sequential Recommendation 25

Oracle-guided Dynamic User Preference Modeling for Sequentia...

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18th ACM International Conference on Web Search and data Mining, WSDM 2025

作者： Xia, Jiafeng Li, Dongsheng Gu, Hansu Lu, Tun Zhang, Peng Shang, Li Gu, Ning Fudan University Shanghai China Microsoft Research Asia Shanghai China Amazon Seattle United States School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China Fudan Institute on Aging MOE Laboratory for National Development and Intelligent Governance Shanghai Institute of Intelligent Electronics & Systems Fudan University Shanghai China

ISBN: (纸本)9798400713293

Sequential recommendation methods can capture dynamic user preferences from user historical interactions to achieve better performance. However, most existing methods only use past information extracted from user historical interactions to train the models, leading to the deviations of user preference modeling. Besides past information, future information is also available during training, which contains the "oracle" user preferences in the future and will be beneficial to model dynamic user preferences. Therefore, we propose an oracle-guided dynamic user preference modeling method for sequential recommendation (Oracle4Rec), which leverages future information to guide model training on past information, aiming to learn "forward-looking" models. Specifically, Oracle4Rec first extracts past and future information through two separate encoders, then learns a forward-looking model through an oracle-guiding module which minimizes the discrepancy between past and future information. We also tailor a two-phase model training strategy to make the guiding more effective. Extensive experiments demonstrate that Oracle4Rec is superior to state-of-the-art sequential methods. Further experiments show that Oracle4Rec can be leveraged as a generic module in other sequential recommendation methods to improve their performance with a considerable margin. © 2025 Copyright held by the owner/author(s).

关键词： dynamic user preference modeling

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